{"data":{"id":"e1606d54-1605-4c23-809e-e84280f98af5","title":"Differentially Private Zeroth-Order Methods for Scalable Large Language Model Fine-Tuning","summary":"This paper studies differentially private (DP) zeroth-order methods for fine-tuning large language models, which approximate gradients to avoid the scalability bottleneck of DP-SGD. It proposes two methods: DP-ZOSO, which dynamically schedules key hyperparameters, and DP-ZOPO, which applies data-free stagewise pruning to reduce trainable parameters without extra privacy budget. The authors report theoretical analysis and empirical results on encoder-only and decoder-only language models across diverse tasks.","solution":"N/A -- no mitigation discussed in source.","labels":["privacy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11457969","publishedAt":"2026-03-30T13:17:27.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-03-30T13:17:27.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"training_data","llmSpecific":true,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}